Our content loss is motivated by the observation that robust pattern
recognition can be built using local self-similarity descriptors
[1]}. An every day example of this is the
phenomenon called pareidolia, where the self-similarity patterns of
inanimate objects are perceived as faces because they match a loose
template (see Figure REF ). Formally, let \(D^O\)  be the pairwise cosine
distance matrix of all (hypercolumn) feature vectors extracted from \(O^{(t)}\) , and
let \(D^{C}\)  be defined analogously for the content image. We visualize several potential rows of \(D^C\)  in Figure REF . We define our content loss as:

Lcontent(O,C) = 1n2 i,j | DOiji DOij- DCiji DCij|

In other words the normalized cosine distance between feature vectors
extracted from any pair of coordinates should remain constant between
the content image and the output image. This constrains the structure
of the output, but allows global translations and rotoreflections in feature space relative to the original content image. As a result semantics and spatial layout are broadly preserved, but colors and texture of our output \(O^{(t)}\)  can
drastically differ from those in \(C\) .
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